README for replication package of the results reported in the paper: "The Gender Promotion Gap: Evidence from Central Banking" (RESTAT MS 22860) by Laura Hospido (laura.hospido@bde.es) & Luc Laeven (Luc.laeven@ecb.int) & Ana Lamo (ana.lamo@ecb.int)
This version: October 2020 

DATA FILES:

There are two ASCII delimited files containing the two datasets used in the analysis. 
�Profiles.cvs� is a dataset that contains information on demographic characteristics and working histories of the European Central Bank (ECB) employees over the period 2003 to 2017. The unit of analysis is the employee by month and year since entry to the ECB.  
 
�Campaigns.cvs� is a dataset that contains information on ECB recruitment campaigns since 2012. For each campaign, the dataset includes information on the characteristics of internal potential candidates, the presence of external candidates and the recruitment campaigns itself.

Both datasets have been compiled by the ECB�s Human Resources department from the personnel records of the ECB, and have been anonymised under the supervision of the ECB data protection office. 

The datasets are proprietary. External researchers should contact the ECB using the contact details below to inquire about access to the data.

The section DATA DICTIONARY below describes the variables included in both datasets.

ACCESS TO DATA: 

The datasets are proprietary. FOr questions about the data and how to get access to the data, please contact Ana Lamo at the ECB, contact info: Ana Lamo, Sonnemannstrasse 20, 60314 Frankfurt am Main, Germany. E-mail: ana.lamo@ecb.int

REPLICATION FILES: 

These replication files read the data in �Profiles.cvs�  and/or �Campaigns.cvs� and have been run using Stata version 15.1

**------------------------------------**
STEP 1: 
Figures 1-3 & A1 of the paper **
dofile "1.HLL Graphs_Profiles.do"
**------------------------------------**
STEP 2: 
Tables 1 & A2 of the paper **
dofile "2.HLL Descriptives.do"
**-------------------------------------**
STEP 3
Tables 2-3, 6 & A3-A5 of the paper **
dofile "3.HLL Profiles regressions.do"
**--------------------------------------**
** STEP 4:
Tables 4-5 & A&-A8 of the paper **
dofile "4.HLL Campaigns regressions.do" 


DATA DICTIONARY:

Dataset 1: Working histories (2003-2017}
File: profiles.csv

This dataset includes demographic characteristics and working histories
of the employees over the period 2003 to 2017. The unit of analysis is the employee by month and year since entry to the ECB. 

Observations:        85,282                          

Variable name    {Variable label}
----------------------------------------   
pid              {Individual identifier}

year             {Year of record}

month            {Month of record}

entry            {Months since entry a the ECB}

salaryband       {Salary band}

It takes the following values: Expert, Principal expert  and Adviser, that correspond to salary band F/G, H and I respectively. 

entry_band       {Salary band at entry}                   

steptotal        {Salary step when aggregating all levels of all bands in one 			scale}      
Measures salary. Number of salary steps, as reported in Table A1 of the Online appendix. Each step corresponds to a salary level in euros, with salaries increasing in the number of steps. The salary steps in our sample of professional staff go from 263 to 544.

female           {Female}

Dummy variable that takes a value of one if the employee is a woman and zero otherwise.

age_round        {Age, five year intervals} 

Age of the employee in intervals of five years.

age1             {Age less than 35}
age2             {Age 35-39}
age3             {Age 40-44}
age4             {Age 45-49}
age5             {Age 50-54}
age6             {Age >54}

Age dummies for age brackets. 

dir1             {directorate A}
dir2             {directorate B}
dir3             {directorate C}
dir4             {directorate D}
dir5             {directorate E}
dir6             {directorate F}
dir7             {directorate G}
dir8             {directorate H}
dir9             {directorate I}

Dummy variables that take a value of one if the employee works in the corresponding directorate (department). The directorates include Economics, Monetary Policy, Market Operations, Market Infrastructure, International,
Financial Stability, Risk Management, Research, and Statistics.  

mtenure         {Tenure within band, months}

Months that the employee has been in the current salary band.

children        {Children}

Dummy variable that takes a value of one if the employee has dependent children, and zero otherwise. 

sumMatAdop      {Paid maternity leave}

Months on paid leave to take care of children since entry. 


sumPLeave       {Unpaid parental leave}

Months on unpaid leave to take care of children since entry.

HHallowance     {Head of household}

Dummy variable that takes a value of one if the employee�s spouse earns less than a certain level (currently e57,211) or if the employee is a single parent.

y2_topPerf      {Top performer}

Dummy variable that takes a value of one if the employee received a salary increase that is among the top 25% in her department at least once in the past two years, and zero otherwise.

y2_mentee       {Mentee}

Dummy variable that takes a value of one if the employee participated in the mentorship program at least once in the past two years, and zero otherwise.


y2_bonus        {Bonus}

Dummy variable that takes a value of one if the employee received cash bonuses at least once in the past two years, and zero otherwise.


Perpromotion    {Permanent promotion}

Dummy variable that takes a value of one if if employee moves to a higher salary band.


sample_promotions {Indicator to select promotions sample}

Dummy variable that takes a value of one for those employees who are Principal experts or Advisors (band H and I) but were not promoted during the time sample and therefore are excluded of the sample when analysing promotions.  

----------------------------------------------------------------------------------------------------------------------------------------------------------

Dataset 2: Recruitment campaigns (2012-2017}
File: campaigns.csv

Observations:        23,209                          

This dataset consists of information on each recruitment campaign since 2012. For each campaign includes information on the characteristics of internal potential candidates, the presence of external candidates and the recruitment campaigns itself. 
                        
Variable name     {Variable label}   
----------------------------------------
pid              {Individual identifier}

year             {Year of record}

month            {Month of record}

campaign         {Recruitment campaign}

candidate        {Applying for promotion}

Dummy variable that takes a value of one if the employee applies for a 
promotion (thus participates in a recruitment campaign)

offer            {Received the offer}

Dummy variable that takes a value of one if the employee receives an offre (thus wins a recruitment campaign)

female           {Female}

Dummy variable that takes a value of one if the employee is a woman and zero otherwise.

mtenure          {Tenure within band, months}

Months that the employee has been in the current salary band.

age1             {Age less than 35}
age2             {Age 35-39}
age3             {Age 40-44}
age4             {Age 45-49}
age5             {Age 50-54}
age6             {Age +54}

Age dummies for age brackets. 

dir1             {directorate A}
dir2             {directorate B}
dir3             {directorate C}
dir4             {directorate D}
dir5             {directorate E}
dir6             {directorate F}
dir7             {directorate G}
dir8             {directorate H}
dir9             {directorate I}

Dummy variables that take a value of one if the employee works in the corresponding directorate (department). The directorates include Economics, Monetary Policy, Market Operations, Market Infrastructure, International,
Financial Stability, Risk Management, Research, and Statistics.  

children        {Children}
Dummy variable that takes a value of one if the employee has dependent children, and zero otherwise. 

sumMatAdop       {Paid maternity leave}

Months on paid leave to take care of children since entry. 

sumPLeave        {Unpaid parental leave}

Months on unpaid leave to take care of children since entry.

HHallowance      {Head of household}

Dummy variable that takes a value of one if the employee�s spouse earns less than a certain level (currently e57,211) or if the employee is a single parent.

y2_topPerf       {Top performer}

Dummy variable that takes a value of one if the employee received a salary increase that is among the top 25% in her department at least once in the past two years, and zero otherwise.

y2_mentee        {Mentee}

Dummy variable that takes a value of one if the employee participated in the mentorship program at least once in the past two years, and zero otherwise.

y2_bonus         {Bonus}

Dummy variable that takes a value of one if the employee received cash bonuses at least once in the past two years, and zero otherwise

com100           {Competition index} 

Fraction of potential candidates for promotion in the division that have a salary at the upper end of the salary band, defined as a salary of above 100 steps (up to a maximum of 169 steps.

Ducom100F        {Female competition} 

Dummy that takes a value one if the number of potential female candidates in the division with a salary of above 100 steps in the salary band is more than the 30 percent of the total pool of potential candidates in the division 

note: the candidate in question is excluded from the calculation of the competition variables.

externalC        {External campaign}

Dummy variable that takes a value of one if the campaign is open to external candidates, and zero otherwise

panelsize        {Size of selection panel} 

Number of panel members on the selection panel. 

gender_composition      {Share of female panellists} 

Share of female panellists is the number of female panel members divided by the total number of panel members.

share_externalcandidates {Share of external candidates}           

Ratio of external candidates to total candidates that have applied to a particular selection campaign.

 